# Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch Dac model.""" import inspect import unittest import numpy as np from datasets import Audio, load_dataset from parameterized import parameterized from transformers import AutoProcessor, DacConfig, DacModel from transformers.testing_utils import ( is_torch_available, require_deterministic_for_xpu, require_torch, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch @require_torch # Copied from transformers.tests.encodec.test_modeling_encodec.EncodecModelTester with Encodec->Dac class DacModelTester: # Ignore copy def __init__( self, parent, batch_size=3, num_channels=1, is_training=False, intermediate_size=1024, encoder_hidden_size=16, downsampling_ratios=[2, 4, 4], decoder_hidden_size=16, n_codebooks=6, codebook_size=512, codebook_dim=4, quantizer_dropout=0.0, commitment_loss_weight=0.25, codebook_loss_weight=1.0, sample_rate=16000, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.is_training = is_training self.intermediate_size = intermediate_size self.sample_rate = sample_rate self.encoder_hidden_size = encoder_hidden_size self.downsampling_ratios = downsampling_ratios self.decoder_hidden_size = decoder_hidden_size self.n_codebooks = n_codebooks self.codebook_size = codebook_size self.codebook_dim = codebook_dim self.quantizer_dropout = quantizer_dropout self.commitment_loss_weight = commitment_loss_weight self.codebook_loss_weight = codebook_loss_weight def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0) config = self.get_config() inputs_dict = {"input_values": input_values} return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def prepare_config_and_inputs_for_model_class(self, model_class): input_values = floats_tensor([self.batch_size, self.num_channels, self.intermediate_size], scale=1.0) config = self.get_config() inputs_dict = {"input_values": input_values} return config, inputs_dict # Ignore copy def get_config(self): return DacConfig( encoder_hidden_size=self.encoder_hidden_size, downsampling_ratios=self.downsampling_ratios, decoder_hidden_size=self.decoder_hidden_size, n_codebooks=self.n_codebooks, codebook_size=self.codebook_size, codebook_dim=self.codebook_dim, quantizer_dropout=self.quantizer_dropout, commitment_loss_weight=self.commitment_loss_weight, codebook_loss_weight=self.codebook_loss_weight, ) # Ignore copy def create_and_check_model_forward(self, config, inputs_dict): model = DacModel(config=config).to(torch_device).eval() input_values = inputs_dict["input_values"] result = model(input_values) self.parent.assertEqual(result.audio_values.shape, (self.batch_size, self.intermediate_size)) @require_torch # Copied from transformers.tests.encodec.test_modeling_encodec.EncodecModelTest with Encodec->Dac class DacModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DacModel,) if is_torch_available() else () is_encoder_decoder = True test_resize_embeddings = False pipeline_model_mapping = {"feature-extraction": DacModel} if is_torch_available() else {} def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): # model does not have attention and does not support returning hidden states inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if "output_attentions" in inputs_dict: inputs_dict.pop("output_attentions") if "output_hidden_states" in inputs_dict: inputs_dict.pop("output_hidden_states") return inputs_dict def setUp(self): self.model_tester = DacModelTester(self) self.config_tester = ConfigTester( self, config_class=DacConfig, hidden_size=37, common_properties=[], has_text_modality=False ) def test_config(self): self.config_tester.run_common_tests() def test_model_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_forward(*config_and_inputs) # TODO (ydshieh): Although we have a potential cause, it's still strange that this test fails all the time with large differences @unittest.skip(reason="Might be caused by `indices` computed with `max()` in `decode_latents`") def test_batching_equivalence(self): super().test_batching_equivalence() def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] # Ignore copy expected_arg_names = ["input_values", "n_quantizers", "return_dict"] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @unittest.skip("The DacModel is not transformers based, thus it does not have `inputs_embeds` logics") def test_inputs_embeds(self): pass @unittest.skip("The DacModel is not transformers based, thus it does not have `inputs_embeds` logics") def test_model_get_set_embeddings(self): pass @unittest.skip("The DacModel is not transformers based, thus it does not have the usual `attention` logic") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip("The DacModel is not transformers based, thus it does not have the usual `attention` logic") def test_attention_outputs(self): pass @unittest.skip("The DacModel is not transformers based, thus it does not have the usual `hidden_states` logic") def test_hidden_states_output(self): pass def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_determinism(first, second): # outputs are not tensors but list (since each sequence don't have the same frame_length) out_1 = first.cpu().numpy() out_2 = second.cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**self._prepare_for_class(inputs_dict, model_class))[0] second = model(**self._prepare_for_class(inputs_dict, model_class))[0] if isinstance(first, tuple) and isinstance(second, tuple): for tensor1, tensor2 in zip(first, second): check_determinism(tensor1, tensor2) else: check_determinism(first, second) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (list, tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif isinstance(tuple_object, dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) def test_identity_shortcut(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() config.use_conv_shortcut = False self.model_tester.create_and_check_model_forward(config, inputs_dict) def test_quantizer_from_latents(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() model = DacModel(config=config).to(torch_device).eval() self.assertTrue( all(hasattr(quantizer, "codebook_dim") for quantizer in model.quantizer.quantizers), msg="All quantizers should have the attribute codebook_dim", ) with torch.no_grad(): encoder_outputs = model.encode(inputs_dict["input_values"]) latents = encoder_outputs.projected_latents quantizer_representation, quantized_latents = model.quantizer.from_latents(latents=latents) self.assertIsInstance(quantizer_representation, torch.Tensor) self.assertIsInstance(quantized_latents, torch.Tensor) self.assertEqual(quantized_latents.shape[0], latents.shape[0]) self.assertEqual(quantized_latents.shape[1], latents.shape[1]) # Copied from transformers.tests.encodec.test_modeling_encodec.normalize def normalize(arr): norm = np.linalg.norm(arr) normalized_arr = arr / norm return normalized_arr # Copied from transformers.tests.encodec.test_modeling_encodec.compute_rmse def compute_rmse(arr1, arr2): arr1_np = arr1.cpu().numpy().squeeze() arr2_np = arr2.cpu().numpy().squeeze() max_length = min(arr1.shape[-1], arr2.shape[-1]) arr1_np = arr1_np[..., :max_length] arr2_np = arr2_np[..., :max_length] arr1_normalized = normalize(arr1_np) arr2_normalized = normalize(arr2_np) return np.sqrt(((arr1_normalized - arr2_normalized) ** 2).mean()) """ Integration tests for DAC. Code for reproducing expected outputs can be found here: - test_integration: https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-test_dac-py - test_batch: https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-test_dac_batch-py See https://github.com/huggingface/transformers/pull/39313 for reason behind large tolerance between for encoder and decoder outputs (1e-3). In summary, original model uses weight normalization, while Transformers does not. This leads to accumulating error. However, this does not affect the quantizer codes, thanks to discretization being robust to precision errors. Moreover, codec error is similar between Transformers and original. Moreover, here is a script to debug outputs and weights layer-by-layer: https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-dac_layer_by_layer_debugging-py """ # fmt: off # -- test_integration EXPECTED_PREPROC_SHAPE = { "dac_16khz": torch.tensor([1, 1, 93760]), "dac_24khz": torch.tensor([1, 1, 140800]), "dac_44khz": torch.tensor([1, 1, 258560]), } EXPECTED_ENC_LOSS = { "dac_16khz": 24.8767, "dac_24khz": 27.6831, "dac_44khz": 23.8870, } EXPECTED_QUANT_CODES = { "dac_16khz": torch.tensor( [ [ [804, 25, 536, 52, 68, 867, 388, 653, 315, 706, 301, 305, 752, 25, 40], [955, 955, 134, 601, 431, 375, 967, 56, 54, 261, 871, 552, 232, 341, 228], [977, 701, 172, 927, 617, 765, 790, 149, 403, 707, 511, 226, 254, 883, 644], [467, 85, 828, 54, 211, 1007, 906, 253, 677, 1007, 302, 577, 644, 330, 778], [189, 865, 586, 321, 116, 357, 911, 865, 1000, 234, 6, 901, 6, 470, 895], [454, 241, 67, 622, 487, 426, 749, 833, 382, 900, 372, 959, 622, 305, 964], [175, 609, 730, 307, 961, 609, 318, 1011, 386, 949, 343, 899, 657, 609, 38], [82, 92, 692, 83, 131, 866, 483, 362, 519, 531, 853, 121, 404, 224, 710], [1003, 260, 431, 460, 827, 927, 81, 76, 629, 298, 168, 177, 466, 741, 762], [196, 203, 594, 394, 198, 560, 952, 437, 222, 992, 934, 316, 497, 31, 538], [129, 715, 393, 635, 246, 716, 908, 384, 962, 873, 92, 254, 592, 496, 83], [257, 502, 606, 204, 993, 428, 176, 395, 901, 323, 342, 849, 226, 453, 513], ] ] ).to(torch_device), "dac_24khz": torch.tensor( [ [ [252, 851, 919, 204, 239, 360, 160, 103, 851, 876, 160, 793, 103, 234, 665], [908, 658, 479, 556, 847, 738, 395, 124, 847, 496, 623, 77, 9, 497, 117], [385, 278, 221, 1020, 408, 330, 70, 215, 80, 84, 320, 998, 931, 470, 944], [383, 259, 271, 348, 179, 304, 634, 282, 788, 542, 356, 760, 297, 903, 623], [487, 159, 414, 947, 608, 685, 101, 74, 372, 823, 417, 866, 671, 589, 901], [692, 175, 508, 54, 85, 383, 787, 629, 844, 7, 511, 382, 383, 643, 134], [652, 895, 846, 766, 326, 640, 852, 365, 887, 126, 216, 224, 568, 1008, 635], [938, 285, 570, 515, 574, 515, 862, 644, 845, 207, 603, 830, 193, 158, 79], [847, 825, 874, 991, 384, 509, 1008, 308, 579, 487, 976, 651, 932, 692, 860], [220, 392, 307, 397, 705, 876, 273, 438, 411, 449, 573, 393, 543, 709, 303], [394, 773, 144, 254, 832, 586, 790, 941, 501, 502, 351, 907, 915, 148, 141], [447, 985, 930, 175, 196, 854, 968, 494, 899, 637, 136, 937, 395, 364, 1000], [677, 690, 428, 756, 471, 225, 763, 124, 333, 23, 821, 644, 635, 130, 475], [932, 589, 436, 548, 555, 53, 466, 280, 598, 689, 400, 194, 73, 619, 450], [592, 402, 177, 731, 693, 618, 871, 177, 761, 222, 927, 986, 676, 197, 658], [192, 560, 368, 729, 626, 656, 174, 271, 383, 345, 381, 567, 467, 970, 794], [834, 92, 990, 380, 146, 286, 644, 929, 173, 292, 1008, 948, 281, 973, 366], [892, 533, 350, 589, 355, 163, 561, 229, 655, 240, 316, 926, 385, 624, 178], [36, 385, 589, 342, 143, 517, 648, 94, 457, 217, 892, 60, 355, 46, 253], [934, 939, 457, 5, 668, 323, 312, 825, 448, 697, 374, 199, 98, 955, 884], [567, 297, 40, 498, 313, 86, 832, 270, 21, 609, 200, 688, 168, 616, 706], [178, 559, 922, 627, 651, 19, 589, 475, 312, 898, 508, 969, 36, 783, 64], [169, 981, 86, 4, 598, 988, 670, 480, 68, 235, 873, 130, 479, 543, 669], [981, 575, 827, 149, 224, 572, 470, 265, 504, 654, 586, 835, 444, 497, 198], [856, 913, 658, 664, 883, 771, 646, 56, 440, 482, 707, 229, 864, 286, 252], [103, 568, 68, 904, 882, 239, 67, 112, 941, 457, 397, 412, 634, 1018, 626], [933, 908, 96, 316, 842, 842, 241, 600, 504, 765, 288, 520, 312, 847, 207], [969, 255, 492, 868, 927, 951, 170, 607, 720, 234, 478, 482, 119, 376, 10], [716, 727, 375, 904, 176, 667, 729, 590, 391, 364, 685, 975, 186, 195, 593], [164, 923, 485, 139, 571, 968, 718, 305, 62, 828, 0, 177, 827, 368, 379], [416, 151, 83, 822, 640, 414, 969, 128, 667, 297, 129, 907, 938, 142, 547], [623, 263, 408, 922, 947, 916, 705, 475, 360, 68, 858, 679, 601, 737, 268], ] ] ).to(torch_device), "dac_44khz": torch.tensor( [ [ [698, 315, 105, 315, 330, 105, 105, 698, 315, 481, 330, 93, 629, 315, 105], [30, 232, 249, 881, 962, 365, 56, 881, 186, 402, 311, 521, 558, 778, 254], [1022, 22, 361, 491, 233, 419, 909, 456, 456, 471, 420, 569, 455, 491, 16], [599, 143, 641, 352, 40, 556, 860, 780, 138, 137, 304, 563, 863, 174, 370], [485, 350, 242, 555, 174, 581, 666, 744, 559, 810, 127, 558, 453, 90, 124], [851, 423, 706, 178, 36, 564, 650, 539, 733, 720, 18, 265, 619, 545, 581], [755, 891, 628, 674, 724, 764, 420, 51, 566, 315, 178, 881, 461, 111, 675], [52, 995, 512, 139, 538, 666, 1017, 868, 619, 0, 449, 1005, 982, 106, 139], [357, 180, 368, 892, 856, 567, 960, 148, 36, 708, 945, 285, 531, 331, 440], ] ] ).to(torch_device), } EXPECTED_DEC_OUTPUTS = { "dac_16khz": torch.tensor([[ 0.0002, 0.0007, 0.0012, 0.0015, 0.0017, 0.0011, 0.0004, -0.0002, -0.0003, 0.0002, 0.0006, 0.0012, 0.0020, 0.0029, 0.0026, 0.0015, 0.0015, 0.0014, 0.0010, 0.0011, 0.0019, 0.0026, 0.0028, 0.0032, 0.0040, 0.0031, 0.0022, 0.0025, 0.0020, 0.0010, 0.0001, 0.0001, 0.0007, 0.0016, 0.0024, 0.0024, 0.0017, 0.0002, -0.0006, -0.0002, 0.0003, 0.0006, 0.0011, 0.0023, 0.0020, 0.0016, 0.0015, 0.0012, 0.0005, -0.0003]]).to(torch_device), "dac_24khz": torch.tensor([[ 1.8275e-04, 1.8167e-04, -3.1626e-05, -6.4468e-05, 2.1254e-04, 8.4161e-04, 1.5839e-03, 1.6693e-03, 1.5439e-03, 1.3923e-03, 1.1167e-03, 6.2019e-04, -1.2014e-04, -5.7301e-04, -1.7829e-04, 6.0980e-04, 6.7130e-04, 1.6166e-04, -6.9366e-06, 3.1507e-04, 6.3976e-04, 7.1702e-04, 6.3391e-04, 5.7553e-04, 1.1151e-03, 1.9032e-03, 1.9737e-03, 1.2812e-03, 5.6187e-04, 3.9073e-04, 3.8875e-04, 3.0256e-04, 3.8140e-04, 7.6331e-04, 1.3098e-03, 1.7796e-03, 2.1707e-03, 2.5330e-03, 2.9214e-03, 3.0557e-03, 2.7402e-03, 2.2303e-03, 1.8196e-03, 1.6796e-03, 1.6199e-03, 1.0460e-03, 3.5502e-04, 2.8095e-04, 3.8291e-04, 2.2683e-04]]).to(torch_device), "dac_44khz": torch.tensor([[ 1.3282e-03, 1.4784e-03, 1.6923e-03, 1.8359e-03, 1.8795e-03, 1.9519e-03, 1.9145e-03, 1.7839e-03, 1.5222e-03, 1.2423e-03, 9.9689e-04, 8.4000e-04, 7.6656e-04, 7.7500e-04, 7.7684e-04, 6.9986e-04, 5.3156e-04, 3.2828e-04, 1.7750e-04, 1.6440e-04, 2.9904e-04, 5.4582e-04, 8.2008e-04, 1.0400e-03, 1.1518e-03, 1.1718e-03, 1.1220e-03, 1.0717e-03, 1.0772e-03, 1.1534e-03, 1.3257e-03, 1.5572e-03, 1.7794e-03, 1.9112e-03, 1.9242e-03, 1.7837e-03, 1.5347e-03, 1.2386e-03, 9.3313e-04, 6.4671e-04, 3.5892e-04, 8.4733e-05, -1.6930e-04, -3.9932e-04, -5.8345e-04, -6.9382e-04, -7.0792e-04, -5.6856e-04, -2.6751e-04, 1.5914e-04]]).to(torch_device), } EXPECTED_QUANT_CODEBOOK_LOSS = { "dac_16khz": 20.7299, "dac_24khz": 22.6602, "dac_44khz": 16.2168, } EXPECTED_CODEC_ERROR = { "dac_16khz": 0.003831653157249093, "dac_24khz": 0.0025609051808714867, "dac_44khz": 0.0007433777209371328, } # -- test_batch EXPECTED_PREPROC_SHAPE_BATCH = { "dac_16khz": torch.tensor([2, 1, 113920]), "dac_24khz": torch.tensor([2, 1, 170880]), "dac_44khz": torch.tensor([2, 1, 313856]), } EXPECTED_ENC_LOSS_BATCH = { "dac_16khz": 20.3752, "dac_24khz": 23.5663, "dac_44khz": 19.5858, } EXPECTED_QUANT_CODES_BATCH = { "dac_16khz": torch.tensor( [ [ [490, 664, 726, 166, 55, 379, 367, 664, 661, 726, 592, 301, 130, 198, 129], [1020, 734, 23, 53, 134, 648, 549, 589, 790, 1000, 449, 271, 1021, 740, 36], [701, 344, 955, 19, 927, 212, 212, 667, 212, 627, 453, 954, 777, 706, 496], [526, 805, 444, 474, 870, 920, 394, 823, 814, 1021, 763, 677, 251, 485, 1021], [721, 134, 280, 439, 287, 77, 175, 902, 973, 412, 739, 953, 130, 75, 543], [675, 316, 285, 341, 783, 850, 131, 487, 701, 150, 749, 730, 900, 481, 498], [377, 37, 237, 489, 55, 246, 427, 456, 755, 1011, 712, 631, 695, 576, 804], [601, 557, 681, 52, 10, 299, 284, 216, 869, 276, 424, 364, 955, 41, 497], [465, 553, 697, 59, 701, 195, 335, 225, 896, 804, 776, 928, 392, 192, 332], [807, 306, 977, 801, 77, 172, 760, 747, 445, 38, 731, 31, 924, 724, 835], [903, 561, 205, 421, 231, 873, 931, 361, 679, 854, 471, 884, 1011, 857, 248], [490, 993, 122, 787, 178, 307, 141, 468, 652, 786, 879, 885, 226, 343, 501], ], [ [140, 320, 210, 489, 444, 320, 210, 73, 821, 1004, 388, 686, 405, 563, 407], [725, 449, 802, 85, 36, 532, 620, 28, 620, 418, 146, 532, 418, 453, 565], [695, 725, 600, 371, 829, 1008, 911, 927, 181, 707, 306, 337, 254, 577, 289], [51, 648, 186, 129, 781, 968, 737, 563, 400, 839, 674, 689, 544, 767, 577], [1007, 234, 145, 966, 734, 748, 68, 272, 473, 973, 414, 586, 618, 6, 909], [410, 566, 507, 756, 943, 1008, 269, 349, 549, 320, 303, 729, 507, 741, 76], [172, 102, 548, 714, 225, 173, 149, 423, 307, 527, 844, 102, 747, 76, 586], [656, 144, 407, 245, 140, 925, 48, 197, 126, 418, 112, 674, 582, 916, 223], [776, 971, 291, 781, 833, 688, 817, 261, 937, 467, 352, 463, 530, 804, 683], [1009, 284, 427, 907, 900, 875, 279, 285, 878, 315, 734, 751, 337, 699, 966], [389, 748, 203, 585, 609, 565, 555, 64, 154, 443, 16, 139, 905, 172, 86], [884, 34, 477, 1013, 335, 493, 724, 202, 356, 199, 728, 552, 755, 223, 371], ], ] ).to(torch_device), "dac_24khz": torch.tensor( [ [ [234, 322, 826, 360, 204, 208, 766, 826, 458, 322, 919, 999, 360, 772, 204], [117, 201, 229, 497, 9, 663, 1002, 243, 556, 300, 781, 496, 77, 780, 781], [554, 342, 401, 553, 728, 196, 181, 109, 949, 528, 39, 558, 180, 5, 197], [112, 408, 186, 933, 543, 829, 724, 1001, 425, 39, 163, 517, 986, 348, 653], [ 88, 207, 671, 551, 742, 231, 870, 577, 353, 1016, 259, 282, 247, 126, 63], [924, 59, 799, 739, 771, 568, 280, 673, 639, 1002, 35, 143, 270, 749, 571], [214, 982, 904, 666, 819, 67, 161, 373, 945, 871, 597, 466, 388, 898, 584], [696, 357, 188, 969, 213, 162, 376, 35, 638, 657, 731, 991, 625, 833, 801], [559, 885, 343, 621, 752, 319, 292, 389, 947, 776, 78, 585, 193, 834, 622], [642, 144, 680, 819, 303, 832, 56, 683, 366, 996, 609, 784, 305, 621, 36], [517, 766, 69, 768, 219, 126, 945, 798, 568, 554, 115, 245, 31, 384, 167], [424, 684, 371, 447, 50, 309, 407, 121, 839, 1019, 816, 423, 604, 489, 738], [274, 490, 578, 353, 517, 283, 927, 432, 464, 608, 927, 32, 240, 852, 326], [737, 226, 450, 862, 549, 799, 887, 925, 392, 841, 539, 633, 351, 7, 386], [624, 497, 586, 937, 516, 898, 768, 188, 420, 173, 116, 602, 786, 940, 56], [430, 927, 322, 885, 367, 175, 691, 337, 21, 796, 317, 826, 109, 604, 54], [917, 854, 118, 231, 567, 332, 827, 422, 339, 958, 529, 63, 992, 597, 428], [468, 619, 605, 598, 912, 1012, 365, 60, 538, 915, 22, 675, 460, 667, 255], [912, 373, 355, 92, 920, 454, 979, 414, 645, 442, 783, 956, 693, 457, 842], [230, 0, 998, 958, 159, 159, 332, 94, 886, 1, 455, 981, 418, 758, 358], [132, 843, 1008, 626, 776, 342, 53, 362, 636, 997, 22, 36, 997, 12, 374], [135, 408, 802, 456, 645, 899, 15, 447, 857, 265, 185, 983, 1018, 282, 607], [272, 467, 461, 358, 389, 792, 385, 339, 50, 888, 63, 3, 792, 588, 972], [179, 180, 212, 656, 60, 73, 261, 644, 755, 496, 137, 948, 879, 361, 863], [739, 588, 948, 452, 297, 1009, 49, 725, 853, 843, 249, 957, 1008, 730, 860], [174, 125, 519, 975, 686, 404, 321, 668, 38, 138, 424, 457, 98, 736, 1004], [ 68, 262, 289, 299, 1022, 170, 865, 869, 951, 839, 100, 301, 828, 62, 511], [509, 693, 235, 208, 668, 777, 284, 832, 376, 203, 784, 101, 344, 587, 736], [121, 83, 484, 951, 839, 180, 801, 363, 890, 373, 206, 467, 524, 572, 614], [146, 297, 674, 895, 740, 179, 782, 521, 721, 815, 85, 74, 179, 650, 554], [708, 166, 203, 1021, 89, 991, 410, 117, 1019, 742, 235, 810, 782, 623, 176], [358, 999, 360, 260, 278, 582, 921, 314, 242, 667, 21, 463, 335, 566, 897], ], [ [851, 360, 851, 877, 665, 322, 581, 936, 826, 910, 110, 110, 160, 103, 204], [325, 260, 722, 260, 549, 20, 508, 455, 221, 631, 846, 658, 457, 124, 496], [529, 367, 767, 408, 628, 190, 80, 460, 351, 209, 768, 255, 655, 759, 605], [344, 192, 255, 271, 402, 930, 805, 939, 497, 94, 843, 38, 96, 140, 760], [415, 65, 953, 337, 599, 358, 520, 477, 602, 539, 443, 703, 124, 110, 92], [514, 847, 606, 1014, 678, 806, 563, 408, 520, 4, 208, 83, 630, 176, 423], [768, 741, 546, 353, 968, 371, 527, 447, 21, 746, 343, 100, 286, 708, 781], [461, 499, 836, 411, 271, 279, 530, 882, 345, 1001, 828, 270, 733, 74, 709], [539, 706, 278, 343, 235, 754, 346, 272, 52, 987, 151, 74, 757, 408, 623], [668, 754, 817, 872, 526, 479, 889, 24, 297, 482, 162, 414, 128, 811, 488], [973, 938, 874, 855, 767, 419, 378, 832, 745, 820, 957, 364, 389, 976, 301], [162, 174, 830, 67, 749, 433, 428, 405, 63, 632, 391, 750, 518, 452, 743], [ 5, 694, 393, 322, 563, 425, 306, 211, 870, 302, 491, 694, 324, 142, 997], [981, 953, 116, 51, 674, 451, 351, 335, 285, 44, 591, 147, 124, 212, 957], [813, 80, 700, 675, 964, 355, 137, 104, 679, 151, 88, 553, 815, 820, 21], [398, 102, 563, 720, 304, 299, 1009, 606, 186, 52, 1012, 807, 999, 642, 901], [405, 522, 668, 526, 657, 762, 624, 636, 358, 570, 572, 169, 580, 567, 939], [153, 712, 786, 553, 210, 472, 327, 759, 51, 153, 833, 22, 800, 777, 283], [324, 45, 757, 563, 703, 888, 256, 447, 515, 313, 94, 345, 295, 596, 132], [792, 242, 242, 225, 229, 1004, 436, 61, 869, 757, 945, 1004, 122, 914, 989], [595, 902, 56, 961, 722, 731, 937, 332, 706, 30, 372, 479, 1023, 837, 513], [918, 972, 772, 658, 594, 12, 106, 225, 678, 920, 971, 724, 181, 864, 837], [672, 237, 87, 36, 344, 866, 260, 473, 915, 203, 385, 23, 561, 754, 71], [327, 65, 330, 525, 115, 837, 384, 734, 113, 178, 982, 285, 678, 392, 50], [206, 317, 201, 954, 534, 692, 902, 773, 399, 215, 766, 143, 35, 135, 672], [483, 984, 864, 843, 478, 811, 931, 656, 561, 636, 638, 326, 141, 140, 632], [508, 315, 204, 862, 265, 444, 277, 658, 281, 1009, 453, 283, 387, 85, 677], [586, 992, 528, 525, 90, 288, 15, 370, 939, 894, 791, 819, 879, 279, 222], [639, 896, 792, 487, 853, 852, 690, 886, 141, 988, 889, 29, 899, 745, 864], [551, 167, 982, 422, 768, 495, 244, 956, 991, 242, 353, 622, 168, 1019, 735], [207, 155, 674, 423, 792, 755, 582, 541, 612, 429, 460, 947, 173, 471, 79], [776, 304, 401, 113, 927, 439, 362, 612, 527, 343, 845, 326, 708, 83, 473], ], ] ).to(torch_device), "dac_44khz": torch.tensor( [ [ [330, 315, 315, 619, 481, 315, 197, 315, 315, 105, 481, 315, 481, 481, 481], [718, 1007, 929, 6, 906, 944, 402, 750, 396, 854, 336, 426, 609, 356, 329], [417, 266, 697, 456, 300, 941, 325, 923, 1022, 605, 991, 7, 939, 217, 456], [813, 811, 271, 148, 184, 838, 723, 497, 678, 922, 12, 333, 918, 842, 285], [832, 307, 635, 794, 334, 828, 32, 505, 610, 170, 161, 907, 193, 372, 585], [ 91, 941, 912, 1001, 507, 486, 362, 1006, 157, 640, 760, 215, 577, 256, 371], [676, 27, 903, 472, 473, 881, 860, 477, 514, 385, 533, 911, 701, 102, 825], [326, 399, 116, 443, 605, 807, 534, 199, 559, 538, 516, 983, 372, 861, 167], [776, 843, 185, 326, 723, 390, 318, 34, 191, 674, 728, 554, 721, 354, 267], ], [ [578, 698, 330, 330, 330, 578, 330, 801, 330, 330, 330, 330, 330, 330, 330], [171, 503, 725, 215, 814, 861, 139, 684, 880, 905, 937, 418, 359, 190, 823], [141, 482, 780, 489, 845, 499, 59, 480, 296, 30, 631, 540, 399, 23, 385], [402, 837, 216, 116, 535, 456, 1006, 969, 994, 125, 1011, 285, 851, 832, 197], [46, 950, 728, 645, 850, 839, 527, 850, 81, 449, 590, 166, 22, 148, 402], [98, 758, 474, 941, 217, 667, 681, 109, 719, 233, 162, 160, 329, 627, 716], [999, 228, 752, 639, 404, 333, 993, 177, 888, 158, 644, 221, 1011, 302, 79], [669, 535, 164, 665, 809, 798, 448, 800, 123, 936, 639, 361, 353, 402, 160], [345, 355, 940, 261, 71, 946, 750, 120, 565, 692, 813, 976, 946, 50, 516], ], ] ).to(torch_device), } EXPECTED_DEC_OUTPUTS_BATCH = { "dac_16khz": torch.tensor([[-1.9537e-04, 1.9159e-04, 3.1591e-04, 2.0804e-04, -3.1973e-05, -3.3672e-04, -4.6511e-04, -4.3928e-04, -2.8604e-04, 2.7375e-04, 8.8118e-04, 1.1193e-03, 1.6241e-03, 1.9374e-03, 1.7826e-03, 5.9879e-04, -4.4053e-04, -1.3708e-03, -1.9989e-03, -2.0518e-03, -1.5591e-03, -4.0491e-04, 6.3700e-04, 1.2456e-03, 1.3381e-03, 1.2848e-03, 6.0356e-04, 9.4392e-05, -6.1609e-04, -1.3806e-03, -1.4977e-03, -9.7825e-04, -3.8692e-04, 5.3131e-04, 1.8666e-03, 2.3713e-03, 2.1134e-03, 1.4220e-03, 7.3615e-04, -2.5369e-04, -9.8636e-04, -1.3868e-03, -1.6701e-03, -1.0521e-03, -6.2109e-04, -5.3288e-04, -2.1532e-04, 4.1671e-04, 7.7438e-04, 8.0039e-04], [ 6.5413e-05, 3.6614e-04, -1.4457e-03, -2.3634e-04, -3.6627e-04, -1.3334e-03, 1.0519e-03, -1.4445e-03, 2.1915e-04, -3.3080e-04, -1.3308e-03, 4.8407e-04, 8.6294e-04, -1.7639e-03, 4.2044e-05, 2.0936e-04, -2.9692e-03, 8.7512e-04, 1.3507e-03, 2.0057e-03, -5.5121e-04, 1.3708e-03, -3.1085e-05, -2.6315e-03, -6.7661e-04, 6.2430e-04, 8.3580e-04, -1.5940e-03, 3.3061e-03, 1.3702e-03, -1.7913e-03, -4.0576e-05, -5.5106e-04, -9.3050e-04, -2.3780e-03, -5.3527e-04, 1.5840e-03, -1.4020e-03, 1.2090e-03, 6.0580e-04, -1.8049e-03, 3.5135e-05, -3.0823e-03, 5.0042e-04, -1.1099e-03, 1.1512e-04, 3.3324e-03, -1.7616e-03, 5.2421e-04, -1.3589e-03]]).to(torch_device), "dac_24khz": torch.tensor([[ 2.5545e-04, 8.9353e-05, -4.1158e-04, -6.1750e-04, -5.9480e-04, -5.6071e-04, -5.2090e-04, -4.2821e-04, -1.4335e-04, -6.9339e-05, -9.0480e-05, 6.5549e-05, 7.5300e-05, 1.9337e-07, 2.0931e-04, 4.1511e-04, 1.1008e-04, -1.6662e-04, 4.9021e-05, 4.0946e-04, 4.3870e-04, 3.9847e-04, 4.1346e-04, 2.3158e-04, 2.4527e-04, 4.4284e-04, 3.8170e-04, 1.2579e-04, -4.0307e-05, -2.8757e-04, -8.5801e-04, -1.4023e-03, -1.5856e-03, -1.5326e-03, -1.5314e-03, -1.4345e-03, -1.0435e-03, -5.2566e-04, 2.8071e-05, 5.4406e-04, 8.9030e-04, 1.0047e-03, 1.0342e-03, 9.4115e-04, 6.8876e-04, 3.2003e-04, -7.9418e-05, -4.0320e-04, -5.7941e-04, -7.3025e-04], [-4.7845e-04, 3.8872e-04, 4.0155e-04, 3.6504e-04, 1.5022e-03, 1.2856e-03, -1.8015e-04, -7.2616e-05, 6.3906e-04, -1.1491e-03, -2.7369e-03, -1.5336e-03, -8.2313e-04, -1.6791e-03, -9.4759e-06, 2.3807e-03, -2.2854e-04, -2.9693e-03, 2.9812e-04, 2.7258e-03, -3.8019e-04, -2.2031e-03, -3.6195e-04, -6.6059e-04, -2.0270e-03, -9.9469e-05, 5.4256e-04, -3.3896e-03, -3.9328e-03, 5.6228e-04, 1.1226e-03, -1.0931e-03, 1.0939e-03, 2.9646e-03, -4.1916e-04, -1.8292e-03, 1.0766e-03, 2.3094e-04, -3.4554e-03, -2.0085e-03, 5.9608e-04, -1.3147e-03, -1.3603e-03, 1.8352e-03, 4.6342e-04, -2.6805e-03, -1.3435e-05, 2.8397e-03, 1.0937e-04, -1.7540e-03]]).to(torch_device), "dac_44khz": torch.tensor([[-4.8139e-04, -2.2367e-04, 3.1570e-06, 1.6349e-04, 2.6632e-04, 3.9803e-04, 5.3275e-04, 7.0730e-04, 8.0937e-04, 9.2120e-04, 1.0271e-03, 1.0728e-03, 1.0603e-03, 1.0328e-03, 9.8452e-04, 8.4670e-04, 6.5249e-04, 4.2936e-04, 1.9743e-04, -4.4033e-06, -1.5679e-04, -2.3475e-04, -2.6826e-04, -2.6645e-04, -2.9844e-04, -3.6448e-04, -4.6388e-04, -5.5712e-04, -6.4478e-04, -7.0090e-04, -7.1978e-04, -6.8389e-04, -6.1487e-04, -4.9192e-04, -3.1528e-04, -1.3920e-04, 1.6591e-05, 1.4938e-04, 2.6723e-04, 4.0855e-04, 6.0641e-04, 8.1632e-04, 9.6742e-04, 1.0481e-03, 1.0581e-03, 1.0213e-03, 9.3807e-04, 8.1994e-04, 6.9299e-04, 5.8774e-04], [ 7.2770e-04, 8.2807e-04, 3.7124e-04, -4.1002e-04, -8.7899e-04, -6.0642e-04, 2.0435e-04, 1.0668e-03, 1.3318e-03, 7.8307e-04, -3.2117e-04, -1.3448e-03, -1.6520e-03, -1.0778e-03, 2.4146e-05, 9.8221e-04, 1.2399e-03, 7.6147e-04, -2.2230e-05, -4.7415e-04, -1.4114e-04, 8.9560e-04, 1.9897e-03, 2.4969e-03, 2.0585e-03, 1.0263e-03, 1.5015e-04, 9.2623e-05, 7.8239e-04, 1.3270e-03, 7.3531e-04, -1.1100e-03, -3.1865e-03, -3.9610e-03, -2.6410e-03, -6.5765e-06, 1.9960e-03, 1.7654e-03, -5.9006e-04, -3.2932e-03, -4.2902e-03, -2.8423e-03, -6.7126e-05, 2.0438e-03, 2.2075e-03, 8.8849e-04, -3.6330e-04, -3.9405e-04, 6.1344e-04, 1.4316e-03]]).to(torch_device), } EXPECTED_QUANT_CODEBOOK_LOSS_BATCH = { "dac_16khz": 20.6472, "dac_24khz": 23.5954, "dac_44khz": 16.1380, } EXPECTED_CODEC_ERROR_BATCH = { "dac_16khz": 0.0019726448226720095, "dac_24khz": 0.0013017073506489396, "dac_44khz": 0.0003825263702310622, } # fmt: on @slow @require_torch class DacIntegrationTest(unittest.TestCase): @parameterized.expand([(model_name,) for model_name in EXPECTED_PREPROC_SHAPE.keys()]) @require_deterministic_for_xpu def test_integration(self, model_name): # load model and processor model_id = f"descript/{model_name}" model = DacModel.from_pretrained(model_id, force_download=True).to(torch_device).eval() processor = AutoProcessor.from_pretrained(model_id) # load audio sample librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_sample = librispeech_dummy[0]["audio"]["array"] # check on processor audio shape inputs = processor( raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt", ).to(torch_device) torch.equal(torch.tensor(inputs["input_values"].shape), EXPECTED_PREPROC_SHAPE[model_name]) with torch.no_grad(): # compare encoder loss encoder_outputs = model.encode(inputs["input_values"]) torch.testing.assert_close( encoder_outputs[0].squeeze().item(), EXPECTED_ENC_LOSS[model_name], rtol=1e-3, atol=1e-3 ) # compare quantizer outputs quantizer_outputs = model.quantizer(encoder_outputs[1]) torch.testing.assert_close( quantizer_outputs[1][..., : EXPECTED_QUANT_CODES[model_name].shape[-1]], EXPECTED_QUANT_CODES[model_name], rtol=1e-6, atol=1e-6, ) torch.testing.assert_close( quantizer_outputs[4].squeeze().item(), EXPECTED_QUANT_CODEBOOK_LOSS[model_name], rtol=1e-4, atol=1e-4 ) # compare decoder outputs decoded_outputs = model.decode(encoder_outputs[1]) torch.testing.assert_close( decoded_outputs["audio_values"][..., : EXPECTED_DEC_OUTPUTS[model_name].shape[-1]], EXPECTED_DEC_OUTPUTS[model_name], rtol=1e-3, atol=1e-3, ) # compare codec error / lossiness codec_err = compute_rmse(decoded_outputs["audio_values"], inputs["input_values"]) torch.testing.assert_close(codec_err, EXPECTED_CODEC_ERROR[model_name], rtol=1e-5, atol=1e-5) # make sure forward and decode gives same result enc_dec = model(inputs["input_values"])[1] torch.testing.assert_close(decoded_outputs["audio_values"], enc_dec, rtol=1e-6, atol=1e-6) @parameterized.expand([(model_name,) for model_name in EXPECTED_PREPROC_SHAPE_BATCH.keys()]) def test_integration_batch(self, model_name): # load model and processor model_id = f"descript/{model_name}" model = DacModel.from_pretrained(model_id).to(torch_device) processor = AutoProcessor.from_pretrained(model_id) # load audio samples librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_samples = [np.array([audio_sample["array"]])[0] for audio_sample in librispeech_dummy[-2:]["audio"]] # check on processor audio shape inputs = processor( raw_audio=audio_samples, sampling_rate=processor.sampling_rate, truncation=False, return_tensors="pt", ).to(torch_device) torch.equal(torch.tensor(inputs["input_values"].shape), EXPECTED_PREPROC_SHAPE_BATCH[model_name]) with torch.no_grad(): # compare encoder loss encoder_outputs = model.encode(inputs["input_values"]) torch.testing.assert_close( encoder_outputs[0].mean().item(), EXPECTED_ENC_LOSS_BATCH[model_name], rtol=1e-3, atol=1e-3 ) # compare quantizer outputs quantizer_outputs = model.quantizer(encoder_outputs[1]) torch.testing.assert_close( quantizer_outputs[1][..., : EXPECTED_QUANT_CODES_BATCH[model_name].shape[-1]], EXPECTED_QUANT_CODES_BATCH[model_name], rtol=1e-6, atol=1e-6, ) torch.testing.assert_close( quantizer_outputs[4].mean().item(), EXPECTED_QUANT_CODEBOOK_LOSS_BATCH[model_name], rtol=1e-4, atol=1e-4, ) # compare decoder outputs decoded_outputs = model.decode(encoder_outputs[1]) torch.testing.assert_close( EXPECTED_DEC_OUTPUTS_BATCH[model_name], decoded_outputs["audio_values"][..., : EXPECTED_DEC_OUTPUTS_BATCH[model_name].shape[-1]], rtol=1e-3, atol=1e-3, ) # compare codec error / lossiness codec_err = compute_rmse(decoded_outputs["audio_values"], inputs["input_values"]) torch.testing.assert_close(codec_err, EXPECTED_CODEC_ERROR_BATCH[model_name], rtol=1e-6, atol=1e-6) # make sure forward and decode gives same result enc_dec = model(inputs["input_values"])[1] torch.testing.assert_close(decoded_outputs["audio_values"], enc_dec, rtol=1e-6, atol=1e-6) @parameterized.expand([(model_name,) for model_name in EXPECTED_PREPROC_SHAPE_BATCH.keys()]) def test_quantizer_from_latents_integration(self, model_name): model_id = f"descript/{model_name}" model = DacModel.from_pretrained(model_id).to(torch_device) processor = AutoProcessor.from_pretrained(model_id) # load audio sample librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) audio_sample = librispeech_dummy[0]["audio"]["array"] # check on processor audio shape inputs = processor( raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt", ).to(torch_device) input_values = inputs["input_values"] with torch.no_grad(): encoder_outputs = model.encode(input_values) latents = encoder_outputs.projected_latents # reconstruction using from_latents quantizer_representation, quantized_latents = model.quantizer.from_latents(latents=latents) reconstructed = model.decode(quantized_representation=quantizer_representation).audio_values # forward pass original_reconstructed = model(input_values).audio_values # ensure forward and decode are the same self.assertTrue( torch.allclose(reconstructed, original_reconstructed, atol=1e-6), msg="Reconstructed codes from latents should match original quantized codes", )